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AI-Powered KR/US Quant Trading Node Analysis System (Vibe Coding)

Starting from a blank starter app, you will build a fully functional investment analysis workflow by manually adding 65 nodes, covering stock filtering, VCP pattern analysis, supply and demand tracking, backtesting, AI commentary, and Telegram notifications. This is a hands-on course where you will connect AI-generated code to actual app functions and complete your own analysis pipeline that covers both the Korean and US markets.

(5.0) 5 reviews

53 learners

Level Basic

Course period Unlimited

Investment
Investment
Quant
Quant
router
router
n8n
n8n
Backtesting
Backtesting
Investment
Investment
Quant
Quant
router
router
n8n
n8n
Backtesting
Backtesting

What you will gain after the course

  • Building an investment analysis system that works by implementing and connecting code generated through AI coding into actual nodes.

  • Design of a 65-node-based analysis workflow including VCP patterns, supply and demand analysis, backtesting, and disclosure/news research

  • Operating a Practical Investment Automation Pipeline Using Telegram Notifications, Automated Scheduling, and DeepSeek AI Analysis



⚠️ Investment Disclaimer

  • This course covers the methodology for anyone to build a KR/US quant trading node analysis system. It was created for the purpose of teaching how to build an automated analysis system utilizing widely used strategies in the current market, such as closing price betting, VCP pattern analysis, Smart Money screening, and ML forecasting. It does not recommend the buying or selling of specific stocks nor does it guarantee investment returns.

  • The returns, win rates, and backtesting results shown in the lecture are simulations based on sample and test data, and past performance does not guarantee future profits.

  • The stock names, scores, and ratings mentioned during the lecture are examples for system demonstration purposes and do not constitute investment recommendations.

  • The investor is solely responsible for all profits and losses resulting from actual investments, and the course creator bears no legal responsibility for the student's investment outcomes.

  • AI and data analysis tools are merely means to assist in investment judgment; final investment decisions must be made based on your own judgment and responsibility.

📋 Notice Regarding Quasi-Investment Advisory Service Registration

  • This lecture and related service provider is a business that has completed the report for pseudo-investment advisory services to the Financial Services Commission in accordance with the "Financial Investment Services and Capital Markets Act."



CREATOR

A build-it-yourself course designed directly by the operator of Hodu's AI Analysis Lab

The operator of <Hodu's AI Analysis Lab>, an AI investment analysis channel with 8,500 subscribers, has broken down the actual structures used for analysis into a starter kit for students.

The goal of this course is not to simply copy and paste chunks of code, but to develop the intuition to expand an analytical system by understanding "why this node is necessary, what data it receives, and what results it passes to the next node."


Video Link: https://www.youtube.com/watch?v=Imxj_T3bilM






FULL FUNCTION MAP

The detailed introduction should show not only the current 16 lectures but also the entire AlphaForge feature set.

The original AlphaForge consists of approximately 65 nodes. Rather than throwing all these features at the student at once, the course is designed so that students unlock each functional group one by one as they create and connect nodes themselves. Starting with the current lessons 1–16, the remaining nodes will be added one by one each week until all approximately 65 nodes are uploaded.



COURSE MAP

Construction of lectures 1 to 16 is currently complete. Following this, similar nodes will be grouped together for expansion.

The original AlphaForge contains approximately 65 nodes. Rather than mechanically splitting the course into 65 individual lessons for every node, the curriculum is structured to group similar nodes into single chapters, teaching students "how to continuously expand." Following the currently released lessons 1–16, new nodes will be added every week until all approximately 65 nodes are eventually reflected in the student curriculum.





WHAT STUDENTS TAKE AWAY

At the end of each lecture, students will have a tangible result that they can actually experiment with and mix.

Each lesson does not end with a single node explanation. By including examples of mixing and running with previous nodes, students can directly see "why this combination created a better candidate."



HOW TO LEARN

Students input prompts, create nodes, and execute them immediately.

Each lecture follows the sequence of "Concept Explanation → Prompt → Generated Node → Execution Result → Connection to the Next Node." Therefore, even those with little coding experience can follow along by looking at the completed screens and results, while experienced students can modify the internal logic of the nodes to expand upon their own strategies.


Financial n8n created with Claude: https://www.youtube.com/watch?v=Imxj_T3bilM

Recommended for
these people

Who is this course right for?

  • A developer who wants to create a functional investment analysis system using AI coding tools.

  • Beginner quant investors who want to expand their own analysis pipelines beyond the prompt level

  • Individual investors and data analysts who want to systematically analyze and automate Korean and US stock market symbols.

Need to know before starting?

  • Understanding of basic programming concepts and Python syntax

  • Experience using AI coding tools (ChatGPT, Cursor, etc.) or prompt engineering skills

  • Basic knowledge of fundamental stock investment terminology (chart patterns, supply and demand, backtesting, etc.)

Hello
This is skysungsisi0926

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Courses

Investment through data, automation completed without coding. Welcome to 'Hodu's AI Analysis Lab.'

Lectures, inquiries, and collaboration: dodu.data@gmail.com

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63 lectures ∙ (2hr 50min)

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